import enum
import shutil
import sys


__a       ,		__a    :	Dict         =    shutil.get_terminal_size()

__a    :	Tuple         =    {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}




class   _UpperCamelCase (		enum.Enum					):


			"""simple docstring"""






			__a    :					Union[str, Any]     					=		0
			__a    :					Tuple     					=		1





def 							UpperCAmelCase		(       lowercase				,     lowercase=""				):







				"""simple docstring"""



				sys.stdout.write(str(lowercase				) + end				)
				sys.stdout.flush()





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=""				):







				"""simple docstring"""



				forceWrite(F"\u001b[{color}m{content}\u001b[0m"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				forceWrite('''\r'''				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				forceWrite(F"\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				forceWrite(''' ''' * TERMINAL_WIDTH				)
				reset_cursor()





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				reset_cursor()
				forceWrite('''-''' * TERMINAL_WIDTH				)
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)